本篇教程来自TensorFlow官网初学者的 TensorFlow 2.0 教程
使用MNIST数据集
下载并安装TensorFlow
from __future__ import absolute_import, division, print_function, unicode_literals
# 安装 TensorFlow
import tensorflow as tf
载入MNIST数据集
将样本从整数转换为浮点数
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
将模型的各层堆叠起来
搭建tf.keras.Sequential模型，为训练选择优化器和损失函数
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
训练并验证模型
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test, verbose=2)